This paper presents a corpus study of evaluative and speculative language. Knowledge of such lang... more This paper presents a corpus study of evaluative and speculative language. Knowledge of such language would be useful in many applications, such a s text categorization and summarization. Analyses of annotator agreement and of characteristics of subjective language are performed. This study yields knowledge needed to design e ective machine learning systems for identifying subjective language.
Subjectivity in natural language refers to aspects of language used to express opinions, evaluati... more Subjectivity in natural language refers to aspects of language used to express opinions, evaluations, and speculations . There are numerous NLP applications for which subjectivity analysis is relevant, including information extraction and text categorization. The goal of this work is learning subjective language from corpora. The rst part of the paper explores annotating subjectivity at di erent levels (expression, sentence, document) and producing annotated corpora. In the second part of the paper, clues of subjectivity are generated and tested, including low-frequency words, collocations, and adjectives and verbs identi ed using distributional similarity. The third part of the paper examines the features working together in concert. The features, generated from di erent datasets using di erent procedures, exhibit consistency in performance in that they all do better and worse on the same datasets. In addition, we show that the density of subjectivity clues in the surrounding context strongly a ects how likely it is that a word is subjective, and give the results of an annotation study assessing the subjectivity of sentences with high-density features. Finally, the clues are used to perform opinionpiece recognition (a type of text categorization and genre detection), to demonstrate the utility of the knowledge acquired in this paper.
This paper presents a corpus study of evaluative and speculative language. Knowledge of such lang... more This paper presents a corpus study of evaluative and speculative language. Knowledge of such language would be useful in many applications, such a s text categorization and summarization. Analyses of annotator agreement and of characteristics of subjective language are performed. This study yields knowledge needed to design e ective machine learning systems for identifying subjective language.
Subjectivity in natural language refers to aspects of language used to express opinions, evaluati... more Subjectivity in natural language refers to aspects of language used to express opinions, evaluations, and speculations . There are numerous NLP applications for which subjectivity analysis is relevant, including information extraction and text categorization. The goal of this work is learning subjective language from corpora. The rst part of the paper explores annotating subjectivity at di erent levels (expression, sentence, document) and producing annotated corpora. In the second part of the paper, clues of subjectivity are generated and tested, including low-frequency words, collocations, and adjectives and verbs identi ed using distributional similarity. The third part of the paper examines the features working together in concert. The features, generated from di erent datasets using di erent procedures, exhibit consistency in performance in that they all do better and worse on the same datasets. In addition, we show that the density of subjectivity clues in the surrounding context strongly a ects how likely it is that a word is subjective, and give the results of an annotation study assessing the subjectivity of sentences with high-density features. Finally, the clues are used to perform opinionpiece recognition (a type of text categorization and genre detection), to demonstrate the utility of the knowledge acquired in this paper.
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Papers by Melanie Martin